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import os |
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import torch |
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import streamlit as st |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import plotly.graph_objects as go |
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input_text = st.text_input( |
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label='Estimate item desirability:', |
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value='I love a good fight.', |
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placeholder='Enter item' |
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) |
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model_path = 'magnolia-psychometrics/item-desirability' |
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auth_token = os.environ.get("item-desirability") or True |
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tokenizer = AutoTokenizer.from_pretrained( |
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pretrained_model_name_or_path=model_path, |
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use_fast=True, |
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use_auth_token=auth_token |
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) |
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model = AutoModelForSequenceClassification.from_pretrained( |
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pretrained_model_name_or_path=model_path, |
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num_labels=1, |
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ignore_mismatched_sizes=True, |
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use_auth_token=auth_token |
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) |
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def z_score(y, mean=.04853076, sd=.9409466): |
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return (y - mean) / sd |
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if input_text: |
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inputs = tokenizer(input_text, padding=True, return_tensors='pt') |
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with torch.no_grad(): |
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score = model(**inputs).logits.squeeze().tolist() |
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z = z_score(score) |
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fig = go.Figure(go.Indicator( |
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mode = "gauge+delta", |
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value = z, |
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domain = {'x': [0, 1], 'y': [0, 1]}, |
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title = f"Item Desirability <br><sup>\"{input_text}\"</sup>", |
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delta = { |
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'reference': 0, |
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'decreasing': {'color': "#ec4899"}, |
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'increasing': {'color': "#36def1"} |
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}, |
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gauge = { |
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'axis': {'range': [-4, 4], 'tickwidth': 1, 'tickcolor': "black"}, |
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'bar': {'color': "#4361ee"}, |
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'bgcolor': "white", |
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'borderwidth': 2, |
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'bordercolor': "#efefef", |
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'steps': [ |
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{'range': [-4, 0], 'color': '#efefef'}, |
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{'range': [0, 4], 'color': '#efefef'}], |
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'threshold': { |
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'line': {'color': "#4361ee", 'width': 8}, |
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'thickness': 0.75, |
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'value': z} |
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})) |
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fig.update_layout( |
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paper_bgcolor = "white", |
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font = {'color': "black", 'family': "Arial"}) |
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st.plotly_chart(fig, theme=None, use_container_width=True) |
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